• Journal of Convergence for Information Technology
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• v.10 no.12
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• pp.201-207
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• 2020

In this study, the practicality of unmanned aerial vehicle photography information was identified. Therefore, a total of four consecutive surveys were conducted on the field-level survey areas among the areas subject to photography using unmanned aerial vehicles, and the changes in crop conditions were analyzed using pictures of unmanned aerial vehicles taken during each survey. It is appropriate to collect and utilize photographic information by directly taking pictures of the survey area according to the time of the on-site survey using unmanned aerial vehicles in the field layer, which is an area where many changes in topography, crop vegetation, and crop types are expected. And it turned out that it was appropriate to utilize satellite images in consideration of economic and efficient aspects in relatively unchanged rice paddies and facilities. If the survey area is well equipped with systems for crop cultivation, deep learning can be utilized in real time by utilizing libraries after obtaining photographic data for a certain area using unmanned aircraft in the future. Through this process, it is believed that it can be used to analyze the overall crop and shipment volume by identifying the crop status and surveying the quantity per unit area.

• Korean journal of applied entomology
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• v.60 no.1
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• pp.135-143
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• 2021

Aerospace and geospatial technologies have become more accessible by researchers and agricultural practitioners, and these technologies can play a pivotal role in transforming current pest management practices in agriculture and forestry. During the past 20 years, technologies including satellites, manned and unmanned aircraft, spectral sensors, information systems, and autonomous field equipment, have been used to detect pests and apply control measures site-specifically. Despite the availability of aerospace and geospatial technologies, along with big-data-driven artificial intelligence, applications of such technologies to apple IPM have not been realized yet. Using a case study conducted at the Korea Apple Research Institute, this article discusses the advances and limitations of current aerospace and geospatial technologies that can be used for improving apple IPM.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.2
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• pp.52-63
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• 2021

In previous research, the coastal wetlands were detected by using the vegetation indices or land cover classification maps derived from the multispectral bands of the satellite or aerial imagery, and this approach caused the various limitations for detecting the coastal wetlands with high accuracy due to the difficulty of acquiring both land cover and topographic information by using the single remote sensing data. This research suggested the efficient methodology for detecting the coastal wetlands using the sentinel-2 satellite image and SRTM(Shuttle Radar Topography Mission) DEM (Digital Elevation Model) acquired in Gomsoman Bay, west coasts of South Korea through the following steps. First, the NDWI(Normalized Difference Water Index) image was generated using the green and near-infrared bands of the given Sentinel-2 satellite image. Then, the binary image that separating lands and waters was generated from the NDWI image based on the pixel intensity value 0.2 as the threshold and the other binary image that separating the upper sea level areas and the under sea level areas was generated from the SRTM DEM based on the pixel intensity value 0 as the threshold. Finally, the coastal wetland map was generated by overlaying analysis of these binary images. The generated coastal wetland map had the 94% overall accuracy. In addition, the other types of wetlands such as inland wetlands or mountain wetlands were not detected in the generated coastal wetland map, which means that the generated coastal wetland map can be used for the coastal wetland management tasks.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.16 no.6
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• pp.63-71
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• 2006

In this paper, we propose a secure solution for user authentication by using fingerprint verification on the sensor-client-server model, even with the client that is not necessarily trusted by the sensor holder or the server. To protect possible attacks launched at the untrusted client, our solution makes the fingerprint sensor validate the result computed by the client for the feature extraction. However, the validation should be simple so that the resource-constrained fingerprint sensor can validate it in real-time. To solve this problem, we separate the feature extraction into binarization and minutiae extraction, and assign the time-consuming binarization to the client. After receiving the result of binarization from the client, the sensor conducts a simple validation to check the result, performs the minutiae extraction with the received binary image from the client, and then sends the extracted minutiae to the server. Based on the experimental results, the proposed solution for fingerprint verification can be performed on the sensor-client-server model securely and in real-time with the aid of an untrusted client.

• Journal of Digital Convergence
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• v.18 no.12
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• pp.29-36
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• 2020

The background of this study is that machine learning administrative services are recently attracting attention as a major policy tool for non-face-to-face administrative services in the post-corona era. This study investigated the types of work expected to be effective when introducing machine learning administrative services for Seoul Metropolitan Government officials who are piloting machine learning administrative services. The research method is a machine that can be introduced by organizational unit by distributing and collecting questionnaires for Seoul administrative organizations that have performed machine learning-based administrative services for one month in July 2020 targeting Seoul public officials using machine learning-based administrative services. By analyzing the learning administration service and application service, the business characteristics of each machine learning administration service type such as supervised learning work type, unsupervised learning work type, and reinforced learning work type were analyzed. As a result of the research analysis, it was found that there were significant differences in the characteristics of administrative tasks by supervised and unsupervised learning areas. In particular, it was found that the reinforcement learning domain contains the most appropriate business characteristics for machine learning administrative services. Implications were drawn. The results of this study can be provided as a reference material to practitioners who want to introduce machine learning administration services, and can be used as basic data for research to researchers who want to study machine learning administration services in the future.

• Journal of the Korea Convergence Society
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• v.12 no.1
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• pp.369-377
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• 2021

Augmented reality smart-glass enables healthcare providers to use patient and their care related information without interference of workflow. In addition, augmented reality smart-glass simulation had advantages in improving competency via remote collaboration and real-time information sharing. This study investigated perception and attitude regarding augmented reality smart glass based healthcare simulation on three different groups of healthcare major students, computer major students, developers and faculties. Using convenience sampling method, data were obtained from 95 participants and statistical analysis were performed using SPSS 25.0. Developer and faculty group showed the highest scores, followed by healthcare major students. There was the high expectation on augmented reality smart-glass for skill acquisition and the high performance and big screen were essential features of device. The findings of this study revealed that differences between healthcare and computer major students exist and strategies to reduce those gaps are required to adopt augmented reality smart glass in healthcare settings.

• Korean journal of applied entomology
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• v.59 no.4
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• pp.371-382
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• 2020

The A, C, F colonies of Apis mellifera ligustica Spin. and D, V colony of Apis mellifera caucasia Gorb. bees were collected from 2005-2007. Consequently, inbred lines were derived from the bees of original colonies by matting in the isolated island with due regard for pure breeding. This project helps in the selection of colonies with higher production capacity, aiming to improve honey and royal jelly production and breeding programs. Twenty-three standard morphological traits of honeybee were evaluated, and samples were compared with the data of the two original subspecies. The result suggested that 8 traits partly preserved in bees of inbred lines, and the bees from A. m. ligustica preserved more traits than bees from A. m. caucasia. Among the studied inbred lines, the F line is distinguished by an increase in leg parameters, considered as a favorable phenotypic trait of inbred lines. Importantly, bred of beelines in the same area can be classified as remote and isolated areas. Therefore, we observed differences of inbred lines with the origin subspecies in description acquired with morphometric characteristics as a result of adaptation, breeding, purebred individual lines used as an important resource for breeding novel cross-breeding colonies.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.125-131
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• 2022

Recently, the prevalence of remotely managed patient care systems in medical institutions is increasing due to COVID-19. In particular, in the case of fluid monitoring, hospitals are considering introducing it as a system that can reduce patient safety and nurses' work. There are two products under development: a load cell method that measures weight and a method that detects drops of sap by infrared sensing. Although each product has differences in operation principle, sensor type, size, usage, and price, medical institutions are highly interested in the accuracy of the data obtained.In this study, two prototypes with different sensor methods were manufactured and the total amount of infusion per hour was measured to test the accuracy, which is the core of the infusion monitoring device. In addition, when there was an external movement, the change in the measured value of the sap was tested to evaluate the accuracy according to the measurement method. As a result of the experiment, there was a difference of less than 5% in the measurement value error of the two devices, and the load cell method showed a difference in the low-capacity measurement value and the infrared method in the high-capacity measurement value. As a result of this experiment, there was little difference in accuracy according to the sensor method of the infusion monitoring device, and it is considered that there is no problem in accuracy when used in a medical institution.

• Journal of the Korean Association of Geographic Information Studies
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• v.24 no.4
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• pp.194-208
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• 2021

The Hwanggang Dam in North Korea is located upstream of the Imjin River which is a shared river in the border area. It is known to have a reservoir capacity of 350 million cubic meters and releases a discharge primarily for generating hydroelectric power and partly for transferring to the Yesung River basin. Due to the supply of water from the Hwanggang Dam to another basin, the flow of the Imjin River has decreased, which has a negative impact on the water supply, river maintenance flow, water quality, and ecological environment in Korea. However, due to the special national security issue of the South and North Korea border region, the hydrological data is not shared, and the operation method of the Hwanggang Dam is unknown, so there is a risk of damage to the southern part of the downstream area. In this study, the monthly diversion as the long-term runoff concept was derived through the calibrated hydrological model based on optical remotely sensed Images and water balance analysis. As a result of the water balance analysis from January 2019 to September 2021, the average diversion of the Hwanggang Dam was 29.2m³/s, which is equivalent to 922 million tons per year and 45.6% of the annual inflow of 2.02 million tons into the Hwanggang Dam.

• Journal of Wetlands Research
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• v.24 no.1
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• pp.38-43
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• 2022

Smart sewage treatment plant means creating a safe and clean water environment by establishing an ICT-based real-time monitoring, remote control management and intelligent system for the entire sewage treatment process. The core technology of such a smart sewage treatment plant can be operation control technology using measuring instruments. This research team analyzed and suggested the operation control technologies necessary for the establishment of the intelligent business by referring to the intelligent research projects of the sewage treatment plant in progress in Korea. As a result of the analysis, a total of six removal technologies were presented, including control by scale, reflow water control, linked treated water control, chemical quantity control, winter operation control, and total organic carbon control. By size, standards that can be classified into small and medium-sized large-scale are presented, and in the case of reflow water control, the location of water quality and flow sensors capable of managing reflow water is suggested. In the case of the linked treated water control, the influence and control points of the linked treated water on the sewage treatment plant were presented, and in the case of the chemical injection volume control, a system capable of optimizing the amount of chemical injection according to the introduction of an intelligent sewage treatment plant was presented. In the case of winter operation, the sensors and pumps to be controlled are suggested when considering the decrease in nitrification due to the decrease in water temperature. In the case of total organic carbon control, an interlocking system considering the total amount of pollution in the future was proposed. These operation control scenarios are expected to be used as basic data to be used in intelligent sewage treatment algorithms and scenarios in the future.